We chose to work on technology which can best help to solve real world challenging problems. We use big data, machine learning, predictive analytics, deep learning, natural language processing, at practically advanced levels.

We chose to work with best-in-breed people around the globe. We hire resources with exceptional background i.e. Tier 1 college and proven
startup experience. IIT, Standford, Berkley, CMU, MIT, Havard, EPFL, NUS, IIM, Wharton, ISB, to name very few of them.

We chose to work for customers with high ambition. We reach clients who are aiming to solve really complex problems.

Milestones

45

Clients

8

Awards

175

Algorithms

315

Books

Teams

Each team at Busigence features its own unique blend of passion, personality, and panache.
Pick the one that most feels like own. Choose your fun

Horizontal

ENGINEERING | SCIENCES | DESIGN | RESEARCH | KNOWLEDGE

Vertical

ENGAGEMENT | DELIVERY | OPERATIONS | ORGANIZATION | STRATEGY

Problems

Problem solving is an art and we are passionate about it

Semantic Similarity

Natural Language

Semantic similarity between words/phrases plays vital role in natural language processing, information retrieval, and artificial intelligence.
This is calculated using information from a structured lexical database and from corpus statistics.

Hash-based Segmentation

Machine Learning

Improving Apriori algorithm by not getting candidates generating. Hash-based technique can reduce the size of candidate itemsets. Each itemset is hashed into a corresponding bucket by using an appropriate hash
function.A bucket can contain different itemsets and it works on minimum support concept.

Classifying Music Listeners

Domain

Requirement analysis on different type of music listeners, with an age group ranging from 16 to 45. The clasification includes savants, enthusiasts, casuals,
indifferents, and few more. Metadata was created for editorial, cultural, & acoustic data points, followed by analsing recommendation methods.

Ontology Processing

Big Data

Graph analysis includes pattern matching to find subgraphs of interest, and graph algorithms such as PageRank and triangle counting.
Apache Spark is used to distribute Graph analysis. GraphFrames support general graph processing, similar to Apache Spark’s GraphX library.

Predicting Missing Values

Predictive Analytics

Missing data needs to be handled during data exploration phase. Prediction model is one of the sophisticated method for handling missing data.
We may have many new observations that have missing values on the independant variables, s random forest model was used to impute missing values.

Skillset

Big Data

Machine Learning

Natural Language

Deep Learning

Internship

This is for (only!) those who have been facing sleepless nights worying about The Right Start